AI-102 Practice Question: Implement natural language processing solutions
A large retail company deploys a custom text classification model using Azure Cognitive Service for Language to categorize customer support tickets into 'Billing', 'Technical', and 'General' categories. The model is trained on 10,000 labeled tickets from the past year. After deployment, the model performs well on new tickets but shows a significant drop in accuracy for tickets submitted during holiday seasons, where the volume of billing issues spikes. The engineering team suspects concept drift. They need to maintain high accuracy without manual retraining every season. Which action should the engineer take?
⚠ Common exam trap
Candidates often think increasing training data or retraining on a schedule is sufficient, but they overlook that active learning with human review is the designed mechanism in Azure Cognitive Service for Language to handle concept drift automatically and continuously.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable active learning on the model and set up a human review loop for low-confidence predictions.
Active learning in Azure Cognitive Service for Language automatically identifies low-confidence predictions and sends them for human review, creating a continuous feedback loop that adapts to concept drift without manual retraining. This allows the model to improve its accuracy on seasonal billing spikes by learning from newly labeled examples, while the human review loop ensures quality control.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add subcategories to the classification schema to better capture seasonal patterns.
Why it's wrong here
More categories increase complexity, not address drift.
- ✓
Enable active learning on the model and set up a human review loop for low-confidence predictions.
Why this is correct
Active learning continuously improves the model with new data.
- ✗
Increase the number of training examples per category to 15,000 each.
Why it's wrong here
More data from the same distribution does not fix drift.
- ✗
Schedule monthly retraining using the original 10,000 tickets plus the new tickets.
Why it's wrong here
Monthly retraining may not be frequent enough for seasonal spikes.
Go deeper
Related to this question
About these practice questions
Courseiva writes every AI-102 question from scratch — 945 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.